Trial reportBioMed research international2018
Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China.
Trial report in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it, 127 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.Reviews in endocrine & metabolic disorders · 2022Pooled it
- Artificial Intelligence in Clinical Nutrition: Current Uses, Challenges, and Opportunities.Nutrients · 2026Review
- Diagnosis and grading of steatotic liver disease via clinical and laboratory data using machine learning.Scientific reports · 2026Article
- Article
- Interpretable Machine Learning Models for Early Detection of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Non-Invasive Routine Clinical and Laboratory Data.International journal of general medicine · 2026Article
- Serum biomarker screening and metabolic profiling analysis of nonalcoholic fatty liver disease patients using untargeted metabolomics and machine learning techniques.Frontiers in molecular biosciences · 2026Article
- Article
- ChatGPT improves usability, effectiveness, scalability, interpretability and accessibility, in early diagnosis of metabolic dysfunction-associated fatty liver disease.BMC gastroenterology · 2025Article
- Rapid Classification and Quantitative Prediction of Aflatoxin BMolecules (Basel, Switzerland) · 2025Article
- Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand.Scientific reports · 2025Article
- Extracellular vesicle-mediated approaches for the diagnosis and therapy of MASLD: current advances and future prospective.Lipids in health and disease · 2025Review
- Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.Frontiers in radiology · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.Archives of Iranian medicine · 2024Article
- Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.Lipids in health and disease · 2024Article
- Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases. Machine learning techniques were introduced to evaluate the optimal predictive clinical model of NAFLD.
methodsA cross-sectional study was performed with subjects who attended a health examination at the First Affiliated Hospital, Zhejiang University. Questionnaires, laboratory tests, physical examinations, and liver ultrasonography were employed. Machine learning techniques were then implemented using the open source software Weka. The tasks included feature selection and classification. Feature selection techniques built a screening model by removing the redundant features. Classification was used to build a prediction model, which was evaluated by the F-measure. 11 state-of-the-art machine learning techniques were investigated.
resultsAmong the 10,508 enrolled subjects, 2,522 (24%) met the diagnostic criteria of NAFLD. By leveraging a set of statistical testing techniques, BMI, triglycerides, gamma-glutamyl transpeptidase (
conclusionNovel machine learning techniques may have screening and predictive value for NAFLD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.